Cluster characteristics, mophine treatment differences, and morphine
receptor expression
Figure 1A-E Cluster specific markers, UMAP, morphine receptor
expression, heatmap
DefaultAssay(results)<-"RNA"
results$merged_clusters<-factor(results$merged_clusters, levels = c("NaiveT_1","NaiveT_2", "NaiveT_3","MemoryT_1","MemoryT_2",
"MemoryT_Polarized_1","MemoryT_Polarized_2","Treg_proliferating",
"CytotoxicT","NK","Naive_B","Memory_B","Plasma","CD14_Mono","cDC","pDC"))
Idents(results)<-results$merged_clusters
DimPlot(results, label = TRUE)+NoLegend()
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/A.pdf", width = 16, height = 9)
DimPlot(results, label = TRUE)+NoLegend()
dev.off()->.
Markers<-FindAllMarkers(results, only.pos=TRUE)
## Calculating cluster NaiveT_1
## Calculating cluster NaiveT_2
## Calculating cluster NaiveT_3
## Calculating cluster MemoryT_1
## Calculating cluster MemoryT_2
## Calculating cluster MemoryT_Polarized_1
## Calculating cluster MemoryT_Polarized_2
## Calculating cluster Treg_proliferating
## Calculating cluster CytotoxicT
## Calculating cluster NK
## Calculating cluster Naive_B
## Calculating cluster Memory_B
## Calculating cluster Plasma
## Calculating cluster CD14_Mono
## Calculating cluster cDC
## Calculating cluster pDC
#filter out anything without a significant adjusted P
Markers<-Markers[Markers$p_val_adj<0.05,]
#saving markers for a table eventually
write.table(Markers,"~/gibbs/DOGMAMORPH/Ranalysis/results/FindAllMakers.tsv",sep = "\t", row.names = FALSE, quote = FALSE)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=Markers, caption = htmltools::tags$caption( style = 'caption-side: top; text-align: center; color:black; font-size:200% ;','Cluster markers'))
top10<-group_by(Markers, cluster)%>%slice_max(avg_log2FC, n=12)%>%select(gene)
## Adding missing grouping variables: `cluster`
top10<-unique(top10$gene)
results<-ScaleData(results, features = top10)
## Centering and scaling data matrix
DoHeatmap(results, top10, disp.min = -2, disp.max = 2)
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/B.pdf", width = 16, height = 9)
DoHeatmap(results, top10, disp.min = -2, disp.max = 2)
dev.off()->.
Idents(results)<-factor(results$merged_clusters, levels = rev(levels(results$merged_clusters)))
#RNA markers dotplot
RNA.marks<-c("CD3D","CD4", "SELL", "CCR7", "TCF7", "JUN","FOS","LTA","LTB","IFNG","IL2","TNF", "GATA3", "RORC", "FOXP3","IL2RA","MKI67",
"CD8A","GZMB", "CCL5", "GNLY","NKG7","MS4A1","CST3","LYZ","S100A4","FCER1A","MS4A7", "FCGR3A")
DotPlot(results, features= RNA.marks) + theme(axis.text.x = element_text(angle = 90))
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/C.pdf", width = 16, height = 9)
DotPlot(results, features= RNA.marks) + theme(axis.text.x = element_text(angle = 90))
dev.off()->.
#ADT markers dotplot
DefaultAssay(results)<-"ADT"
results<-NormalizeData(results, normalization.method = "CLR")
## Normalizing across features
protein.marks<-c("CD3-TotalA","CD4-TotalA","CD45RO-TotalA", "CD45RA-TotalA", "CD27-TotalA", "HLA-DR-DP-DQ-TotalA", "CD183-TotalA","CX3CR1-TotalA","CD196-TotalA",
"CD195-TotalA","CD194-TotalA", "CD279-TotalA","TIGIT-TotalA", "CD8-TotalA", "CD56-TotalA","NKp80-TotalA", "CD19-TotalA",
"IgD-TotalA", "CD11b-TotalA","CD11c-TotalA", "CD86-TotalA","CD1c-TotalA")
DotPlot(results, features= protein.marks) + theme(axis.text.x = element_text(angle = 90))
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/D.pdf", width = 16, height = 9)
DotPlot(results, features= protein.marks) + theme(axis.text.x = element_text(angle = 90))
dev.off()->.
Figure 1F Cluster imbalance in treatment
Idents(results)<-results$merged_clusters
meta<-results@meta.data
meta$Treatment<-factor(meta$Treatment, levels = c("Methadone","Bup.Nalo","Naltrexone"))
meta$Timepoint<-factor(meta$Timepoint, levels = c(0,3))
meta_tots_by_part<-group_by(meta, Treatment,Timepoint, Participant, merged_clusters, .drop=FALSE)%>%summarise(n=n())%>%mutate(prop=n/sum(n))%>%ungroup()
## `summarise()` has grouped output by 'Treatment', 'Timepoint', 'Participant'.
## You can override using the `.groups` argument.
meta_tots_by_part<-meta_tots_by_part[!is.nan(meta_tots_by_part$prop),]
meta_tots_by_part<-split(meta_tots_by_part, meta_tots_by_part$Timepoint)
meta_tots_by_part$`3`<-meta_tots_by_part$`3`[, c("Treatment","Participant","merged_clusters","prop")]
meta_tots_by_part$`0`<-meta_tots_by_part$`0`[, c("Treatment","Participant","merged_clusters","prop")]
colnames(meta_tots_by_part$`3`)<-c("Treatment","Participant","merged_clusters","prop3")
meta_tots_by_part<-merge(meta_tots_by_part$`0`,meta_tots_by_part$`3`, by=c("Treatment","Participant","merged_clusters") )
meta_tots_by_part$FC<-log2(meta_tots_by_part$prop3/meta_tots_by_part$prop)
meta_tots_by_part$clip<-meta_tots_by_part$FC
meta_tots_by_part$clip[meta_tots_by_part$clip>3]<-3
meta_tots_by_part$clip[meta_tots_by_part$clip< -3]<- -3
ggplot(meta_tots_by_part, aes(x=merged_clusters, y=clip, group=Treatment, color=Treatment))+geom_point(position=position_dodge(width = 0.5), size=3)+theme_classic()+theme(axis.text.x = element_text(angle = 90, vjust = 0.3, hjust = 1, size = 16))+geom_hline(yintercept = 0, linetype=3)+ylab("log2(FC)")+xlab("Cluster")+ylim(c(-3.5,3.5))+ggtitle("Clipped Log2FC cluster proportions")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
meta_tots_by_part$FC[is.infinite(meta_tots_by_part$FC)]<-0
meta_tots_by_part$FC[is.nan(meta_tots_by_part$FC)]<-0
twoway.anova<-aov(FC ~ Treatment * merged_clusters, meta_tots_by_part)
#p value now significant on the transformed values, follow ups justified
summary(twoway.anova)
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 2 7.25 3.624 4.116 0.01827 *
## merged_clusters 15 31.40 2.094 2.378 0.00432 **
## Treatment:merged_clusters 30 47.51 1.584 1.798 0.01215 *
## Residuals 144 126.79 0.881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#follow up comparisons
clusters<-names(table(meta_tots_by_part$merged_clusters))
treatments<-names(table(meta_tots_by_part$Treatment))
for(i in clusters){
for(j in 1:3){
if(j==3){
k=1
}else{
k=j+1
}
resultst<-wilcox.test(meta_tots_by_part$FC[meta_tots_by_part$merged_clusters==i & meta_tots_by_part$Treatment==treatments[j]],meta_tots_by_part$FC[meta_tots_by_part$merged_clusters==i & meta_tots_by_part$Treatment==treatments[k]])
if(resultst$p.value<0.05){
print(i)
print(paste(treatments[j], "vs", treatments[k]))
print(resultst)
}
}
}
## [1] "NaiveT_1"
## [1] "Naltrexone vs Methadone"
##
## Wilcoxon rank sum exact test
##
## data: meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[j]] and meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[k]]
## W = 0, p-value = 0.02857
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "NaiveT_3"
## [1] "Methadone vs Bup.Nalo"
##
## Wilcoxon rank sum exact test
##
## data: meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[j]] and meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[k]]
## W = 16, p-value = 0.02857
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "CytotoxicT"
## [1] "Bup.Nalo vs Naltrexone"
##
## Wilcoxon rank sum exact test
##
## data: meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[j]] and meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[k]]
## W = 0, p-value = 0.02857
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "CytotoxicT"
## [1] "Naltrexone vs Methadone"
##
## Wilcoxon rank sum exact test
##
## data: meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[j]] and meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[k]]
## W = 16, p-value = 0.02857
## alternative hypothesis: true location shift is not equal to 0
## Warning in
## wilcox.test.default(meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == :
## cannot compute exact p-value with ties
## Warning in
## wilcox.test.default(meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == :
## cannot compute exact p-value with ties
## [1] "CD14_Mono"
## [1] "Methadone vs Bup.Nalo"
##
## Wilcoxon rank sum exact test
##
## data: meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[j]] and meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[k]]
## W = 16, p-value = 0.02857
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "CD14_Mono"
## [1] "Bup.Nalo vs Naltrexone"
##
## Wilcoxon rank sum exact test
##
## data: meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[j]] and meta_tots_by_part$FC[meta_tots_by_part$merged_clusters == i & meta_tots_by_part$Treatment == treatments[k]]
## W = 0, p-value = 0.02857
## alternative hypothesis: true location shift is not equal to 0
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/F.pdf", width = 16, height = 9)
ggplot(meta_tots_by_part, aes(x=merged_clusters, y=clip, group=Treatment, color=Treatment))+geom_point(position=position_dodge(width = 0.5), size=3)+theme_classic()+theme(axis.text.x = element_text(angle = 90, vjust = 0.3, hjust = 1, size = 16))+geom_hline(yintercept = 0, linetype=3)+ylab("log2(FC)")+xlab("Cluster")+ylim(c(-3.5,3.5))+ggtitle("Clipped Log2FC cluster proportions")
## Warning: Removed 2 rows containing missing values (`geom_point()`).
dev.off()->.
Figure 1G Maximum differences across DE
data<-readRDS("~/gibbs/DOGMAMORPH/Ranalysis/Objects/20230702AllClustersTreatmentconditionsRNA.rds")
total_DE<-c()
for(cluster in data){
DEs=0
for (comparison in cluster){
DEs=DEs+sum(comparison$p_val_adj<=0.05)
}
total_DE<-c(total_DE, DEs)
}
names(total_DE)<-names(data)
print(total_DE)
## MemoryT_Polarized_1 NaiveT_1 Naive_B pDC
## 500 699 129 35
## CytotoxicT Memory_B NaiveT_3 NK
## 677 24 535 133
## Treg_proliferating MemoryT_1 MemoryT_Polarized_2 CD14_Mono
## 298 414 413 125
## cDC NaiveT_2 MemoryT_2 Plasma
## 75 125 5 3
data<-unlist(data, recursive = FALSE)
cond<-names(data)
totalDE<-function(dataframe){
return(sum(dataframe$p_val_adj<0.05))
}
totalDifferentiallyExpressed<-unlist(lapply(data, totalDE))
res<-data.frame(cond,totalDifferentiallyExpressed)
res$cluster<-gsub(paste(c(".Methadone_3_vs_Methadone_0", ".Bup.Nalo_3_vs_Bup.Nalo_0", ".Naltrexone_3_vs_Naltrexone_0", ".Methadone_3_vs_Bup.Nalo_3",
".Bup.Nalo_3_vs_Naltrexone_3", ".Naltrexone_3_vs_Methadone_3" , ".Methadone_0_vs_Bup.Nalo_0" , ".Bup.Nalo_0_vs_Naltrexone_0" ,
".Naltrexone_0_vs_Methadone_0"), collapse = "|"),"",res$cond)
res$comp<-gsub(paste(names(table(Idents(results))),".", collapse = "|", sep = ""),"",res$cond)
res$comp<-gsub("CD14\\+_Mono.","", res$comp)
res$modality<-"RNA"
res$cluster<-factor(res$cluster, levels = levels(results$merged_clusters))
ggplot(res, aes(x=cluster, y=totalDifferentiallyExpressed, fill=comp))+geom_col(position="dodge")+theme_classic()+theme(axis.text.x = element_text(vjust = 0.3, angle = 90, hjust=1))
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/G.pdf", width = 16, height = 9)
ggplot(res, aes(x=cluster, y=totalDifferentiallyExpressed, fill=comp))+geom_col(position="dodge")+theme_classic()+theme(axis.text.x = element_text(vjust = 0.3, angle = 90, hjust=1))
dev.off()->.
DefaultAssay(results)<-"RNA"
opioid.genes<-c("OPRM1", "OGFR","OPRK1","OPRD1","OPRL1")
results$T_Tp<-paste(results$Treatment, results$Timepoint, sep = "_")
results$T_Tp<-factor(results$T_Tp, levels = c("Methadone_0","Methadone_3","Bup.Nalo_0", "Bup.Nalo_3", "Naltrexone_0", "Naltrexone_3"))
tlevels<-rev(c("Methadone_0","Methadone_3","Bup.Nalo_0", "Bup.Nalo_3", "Naltrexone_0", "Naltrexone_3"))
results$group<-paste(results$merged_clusters, results$T_Tp, sep="_")
clusters<-rev(levels(results$merged_clusters))
new_order<-c()
for(i in clusters){
new_order<-c(new_order, unlist(paste(i,tlevels, sep = "_" )))
}
results<-SplitObject(results, "group")
results<-results[new_order]
results_new<-merge(results[[1]],c(results[[2]],results[[3]],results[[4]],results[[5]],results[[6]],results[[7]],results[[8]],results[[9]],results[[10]],
results[[11]],results[[12]],results[[13]],results[[14]],results[[15]],results[[16]],results[[17]],results[[18]],results[[19]],results[[20]],
results[[21]],results[[22]],results[[23]],results[[24]],results[[25]],results[[26]],results[[27]],results[[28]],results[[29]],results[[30]],
results[[31]],results[[32]],results[[33]],results[[34]],results[[35]],results[[36]],results[[37]],results[[38]],results[[39]],results[[40]],
results[[41]],results[[42]],results[[43]],results[[44]],results[[45]],results[[46]],results[[47]],results[[48]],results[[49]],results[[50]],
results[[51]],results[[52]],results[[53]],results[[54]],results[[55]],results[[56]],results[[57]],results[[58]],results[[59]],results[[60]],
results[[61]],results[[62]],results[[63]],results[[64]],results[[65]],results[[66]],results[[67]],results[[68]],results[[69]],results[[70]],
results[[71]],results[[72]],results[[73]],results[[74]],results[[75]],results[[76]],results[[77]],results[[78]],results[[79]],results[[80]],
results[[81]],results[[82]],results[[83]],results[[84]],results[[85]],results[[86]],results[[87]],results[[88]],results[[89]],results[[90]],
results[[91]],results[[92]],results[[93]],results[[94]],results[[95]],results[[96]]))
Idents(results_new)<-results_new$merged_clusters
DotPlot(results_new, features = opioid.genes, split.by = "T_Tp",cols = BottleRocket3)
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig1/E.pdf", width = 16, height = 9)
DotPlot(results_new, features = opioid.genes, split.by = "T_Tp", cols = BottleRocket3)
dev.off()->.
devtools::session_info()
## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.0 (2022-04-22)
## os Red Hat Enterprise Linux 8.9 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C.UTF-8
## ctype C.UTF-8
## tz Etc/UTC
## date 2024-02-03
## pandoc 3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.2.0)
## BiocGenerics 0.44.0 2022-11-01 [1] Bioconductor
## BiocParallel 1.32.6 2023-03-17 [1] Bioconductor
## Biostrings 2.66.0 2022-11-01 [1] Bioconductor
## bitops 1.0-7 2021-04-24 [2] CRAN (R 4.2.0)
## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.2.0)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
## cluster 2.1.4 2022-08-22 [2] CRAN (R 4.2.0)
## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.2.0)
## colorspace 2.1-0 2023-01-23 [2] CRAN (R 4.2.0)
## cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [2] CRAN (R 4.2.0)
## crosstalk 1.2.0 2021-11-04 [2] CRAN (R 4.2.0)
## data.table 1.14.8 2023-02-17 [2] CRAN (R 4.2.0)
## DBI 1.1.3 2022-06-18 [2] CRAN (R 4.2.0)
## deldir 1.0-6 2021-10-23 [2] CRAN (R 4.2.0)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## digest 0.6.31 2022-12-11 [2] CRAN (R 4.2.0)
## dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
## DT 0.28 2023-05-18 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.2.0)
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## GenomeInfoDb 1.34.9 2023-02-02 [1] Bioconductor
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## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
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## pkgbuild 1.4.0 2022-11-27 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.2.0)
## pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.2.0)
## plotly 4.10.1 2022-11-07 [1] CRAN (R 4.2.0)
## plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.0)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
## polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.2.0)
## processx 3.8.1 2023-04-18 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## progressr 0.13.0 2023-01-10 [1] CRAN (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.2.0)
## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
## purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0)
## R6 2.5.1 2021-08-19 [2] CRAN (R 4.2.0)
## RANN 2.6.1 2019-01-08 [2] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [2] CRAN (R 4.2.0)
## Rcpp 1.0.10 2023-01-22 [1] CRAN (R 4.2.0)
## RcppAnnoy 0.0.20 2022-10-27 [1] CRAN (R 4.2.0)
## RcppRoll 0.3.0 2018-06-05 [2] CRAN (R 4.2.0)
## RCurl 1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
## remotes 2.4.2 2021-11-30 [2] CRAN (R 4.2.0)
## reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.2.0)
## reticulate 1.28 2023-01-27 [1] CRAN (R 4.2.0)
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
## rmarkdown 2.22 2023-06-01 [1] CRAN (R 4.2.0)
## ROCR 1.0-11 2020-05-02 [2] CRAN (R 4.2.0)
## Rsamtools 2.14.0 2022-11-01 [1] Bioconductor
## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0)
## Rtsne 0.16 2022-04-17 [2] CRAN (R 4.2.0)
## S4Vectors 0.36.2 2023-02-26 [1] Bioconductor
## sass 0.4.5 2023-01-24 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## scattermore 0.8 2022-02-14 [1] CRAN (R 4.2.0)
## sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.2.0)
## Seurat * 4.3.0 2022-11-18 [1] CRAN (R 4.2.0)
## SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.2.0)
## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.0)
## Signac * 1.9.0 2022-12-08 [1] CRAN (R 4.2.0)
## sp 1.6-0 2023-01-19 [1] CRAN (R 4.2.0)
## spatstat.data 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.explore 3.1-0 2023-03-14 [1] CRAN (R 4.2.0)
## spatstat.geom 3.1-0 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.random 3.1-4 2023-03-13 [1] CRAN (R 4.2.0)
## spatstat.sparse 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.utils 3.0-2 2023-03-11 [1] CRAN (R 4.2.0)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.0)
## tensor 1.5 2012-05-05 [2] CRAN (R 4.2.0)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.2.0)
## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.0)
## XVector 0.38.0 2022-11-01 [1] Bioconductor
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
## zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
##
## [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
## [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
##
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